Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach

Haoyu Han, Juanhui Li, Wei Huang, Xianfeng Tang, Hanqing Lu, Chen Luo, Hui Liu, Jiliang Tang

Keywords: Graph Neural Networks, Node-wise Filters, Node Classification, Homophilic and Heterophilic graphs

Abstract: Graph Neural Networks (GNNs) have proven to be highly effective for node classification tasks across diverse graph structural patterns. Traditionally, GNNs employ a uniform global filter—typically a low-pass filter for homophilic graphs and a high-pass filter for heterophilic graphs. However, real-world graphs often exhibit a complex mix of homophilic and heterophilic patterns, rendering a single filter approach suboptimal. In this work, we theoretically demonstrate that a global filter optimized for one pattern can adversely affect performance on nodes with differing patterns. To address this, we introduce a novel GNN framework Node-MoE that utilizes a mixture of experts to adaptively select the appropriate filters for different nodes. Extensive experiments demonstrate the effectiveness of the proposed Node-MoE on both homophilic and heterophilic graphs.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 8629